BEGINNLP-Beginner Friendly Natural Language Processing Library for Python

Abstract

Natural Language Processing is the most useful tool in the current dynamics where Artificial Intelligence is becoming an integral part of everything we do today. With beginnlp we are making an effort to make this world of NLP more accessible to beginner level tech enthusiasts to begin with NLP with prebuilt easy to you functions like Text Summarization, Keyword Extraction, Translation, Name Entity Recognition, Grammar and Spelling correction, Text Preprocessing and more.

Introduction

Artificial Intelligence (AI) is increasingly integrated into daily life, making Natural Language Processing (NLP) a critical skill for future developers. NLP enables machines to understand, interpret, and generate human language, powering applications such as chatbots, translation, summarization, and voice assistants. However, mainstream NLP libraries can be complex and scattered, posing a challenge for beginners.

The library beginnlp addresses this by providing an easy-to-use, all-in-one solution for essential NLP tasks, including text summarization (abstractive and extractive), grammar and spell checking, text preprocessing, named entity recognition, keyword extraction, translation, speech-to-text, and text-to-speech. It leverages established models and APIs such as T5, spaCy, keyBERT, Whisper, and Coqui TTS, while emphasizing simplicity and modularity to reduce coding complexity.

Testing showed the library performs effectively across casual and academic text use cases, making NLP more accessible to beginners. Limitations include reliance on internet for some features, longer processing times for certain models, dependency on multiple libraries, and opportunities for expanding datasets and offline capabilities. Future improvements could address these issues and enhance accuracy, speed, and offline functionality.

Conclusion

In conclusion we can see that through the made easy functions it Is easier for beginner developers to smoothen out the learning curve for Natural Language Processing. It provides the users with easily accessible feature which are used in day to day Natural Language Processing like text preprocessing, keyword extraction etc. For future we can work on fixing the limitations of the project.

References

[1] Y. Zhang, Y. Zhang, P. Qi, C. D. Manning, and C. P. Langlotz, “Biomedical and clinical English model packages for the Stanza Python NLP library,” Journal of the American Medical Informatics Association, vol. 28, no. 9, pp. 1892–1899, Sep. 2021, doi: https://doi.org/10.1093/jamia/ocab090 [2] D. Kartsaklis et al., “lambeq: An Efficient High-Level Python Library for Quantum NLP,” arXiv:2110.04236 [quant-ph], Oct. 2021, Available: https://arxiv.org/abs/2110.04236 [3] A. Petukhova and N. Fachada, “TextCL: A Python package for NLP preprocessing tasks,” SoftwareX, vol. 19, p. 101122, Jul. 2022, doi: https://doi.org/10.1016/j.softx.2022.101122. [4] J. R. Jim, M. Apon, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, pp. 100059–100059, Feb. 2024, doi: https://doi.org/10.1016/j.nlp.2024.100059. [5] L. Qin et al., “Large Language Models Meet NLP: A Survey,” arXiv.org, 2024. https://arxiv.org/abs/2405.12819 [6] Teja Reddy Gatla, “A Groundbreaking Research in Breaking Language Barriers: NLP And Linguistics Development,” International Journal of Advanced Research and Interdisciplinary Scientific Endeavours, vol. 1, no. 1, pp. 1–7, 2024, doi: https://doi.org/10.61359/11.2206-2401. [7] S. Dai et al., “AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks,” Deleted Journal, vol. 5, no. 1, pp. 13–21, Jun. 2024, doi: https://doi.org/10.60087/jaigs.v5i1.149. [8] C. Thomson, E. Reiter, and A. Belz, “Common Flaws in Running Human Evaluation Experiments in NLP,” Computational Linguistics, vol. 50, no. 2, pp. 795–805, 2024, doi: https://doi.org/10.1162/coli_a_00508. [9] C. Si, D. Yang, and T. Hashimoto, “Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers,” arXiv.org, 2024. https://arxiv.org/abs/2409.04109 [10] F. M. Plaza-del-Arco, A. Curry, A. C. Curry, and D. Hovy, “Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions,” arXiv.org, 2024. https://arxiv.org/abs/2403.01222

Copyright

Copyright © 2025 Vighnesh K. Gupta, Yash Wadhwani, Manasvi Vinnu, Vihan Saraf, Srujal Vispute, Aditya Wagh, Prof. Geeta Zaware. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.